




'''simple docstring'''







import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return 1.0 / (1.0 + np.exp(-_outputs						))







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     np.max(_outputs      ,axis=-1      ,keepdims=UpperCAmelCase						)
   A__       =     np.exp(_outputs - maxes						)
   return shifted_exp / shifted_exp.sum(axis=-1      ,keepdims=UpperCAmelCase						)

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Any						=					'''sigmoid'''
 __snake_case:							Any						=					'''softmax'''
 __snake_case:							Optional[Any]						=					'''none'''




@add_end_docstrings(
    UpperCAmelCase       , R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''       , )
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Dict						=					False
 __snake_case:							str						=					ClassificationFunction.NONE






 def __init__(self       :  Dict    ,		**a       :  Any    )       ->   Union[str, Any]:






    """simple docstring"""


    super().__init__(**a    )

    self.check_model_type(
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
        if self.framework == 'tf'
        else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  List[Any]=None    ,		a       :  Optional[int]=None    ,		a       :  Tuple=""    ,		**a       :  Optional[int]    )       ->   List[str]:






    """simple docstring"""


    A__       =     tokenizer_kwargs

    A__       =     {}
    if hasattr(self.model.config    ,		'return_all_scores'    ) and return_all_scores is None:
       A__       =     self.model.config.return_all_scores

    if isinstance(a    ,		a    ) or top_k is None:
       A__       =     top_k
       A__       =     False
    elif return_all_scores is not None:
       warnings.warn(
           '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
           ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'    ,		a    ,		)
       if return_all_scores:
          A__       =     None
       else:
          A__       =     1

    if isinstance(a    ,		a    ):
       A__       =     ClassificationFunction[function_to_apply.upper()]

    if function_to_apply is not None:
       A__       =     function_to_apply
    return preprocess_params, {}, postprocess_params






 def __call__(self       :  Any    ,		*a       :  List[Any]    ,		**a       :  Union[str, Any]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     super().__call__(*a    ,		**a    )
    # TODO try and retrieve it in a nicer way from _sanitize_parameters.
    A__       =     'top_k' not in kwargs
    if isinstance(args[0]    ,		a    ) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
    else:
       return result






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Optional[Any]    ,		**a       :  Dict    )       ->   Dict[str, GenericTensor]:






    """simple docstring"""


    A__       =     self.framework
    if isinstance(a    ,		a    ):
       return self.tokenizer(**a    ,		return_tensors=a    ,		**a    )
    elif isinstance(a    ,		a    ) and len(a    ) == 1 and isinstance(inputs[0]    ,		a    ) and len(inputs[0]    ) == 2:
       # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
       return self.tokenizer(
           text=inputs[0][0]    ,		text_pair=inputs[0][1]    ,		return_tensors=a    ,		**a    )
    elif isinstance(a    ,		a    ):
       # This is likely an invalid usage of the pipeline attempting to pass text pairs.
       raise ValueError(
           'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
           ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'    )
    return self.tokenizer(a    ,		return_tensors=a    ,		**a    )






 def 						_UpperCamelCase (self       :  str    ,		a       :  Union[str, Any]    )       ->   List[str]:






    """simple docstring"""


    return self.model(**a    )






 def 						_UpperCamelCase (self       :  int    ,		a       :  int    ,		a       :  int=None    ,		a       :  Union[str, Any]=1    ,		a       :  Optional[int]=True    )       ->   Union[str, Any]:






    """simple docstring"""


    if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
          A__       =     ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
          A__       =     ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config    ,		'function_to_apply'    ) and function_to_apply is None:
          A__       =     self.model.config.function_to_apply
       else:
          A__       =     ClassificationFunction.NONE

    A__       =     model_outputs['logits'][0]
    A__       =     outputs.numpy()

    if function_to_apply == ClassificationFunction.SIGMOID:
       A__       =     sigmoid(a    )
    elif function_to_apply == ClassificationFunction.SOFTMAX:
       A__       =     softmax(a    )
    elif function_to_apply == ClassificationFunction.NONE:
       A__       =     outputs
    else:
       raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""    )

    if top_k == 1 and _legacy:
       return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

    A__       =     [
        {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a    )
    ]
    if not _legacy:
       dict_scores.sort(key=lambda a    : x["score"]    ,		reverse=a    )
       if top_k is not None:
          A__       =     dict_scores[:top_k]
    return dict_scores








'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


